The lavListInspect() and lavListTech() functions can be used to
inspect/extract information that is stored inside (or can be computed from) a
lavaanList object.
lavListInspect(object, what = "free", add.labels = TRUE,
add.class = TRUE, list.by.group = TRUE,
drop.list.single.group = TRUE)lavListTech(object, what = "free", add.labels = FALSE,
add.class = FALSE, list.by.group = FALSE,
drop.list.single.group = FALSE)
Character. What needs to be inspected/extracted? See Details for a
full list. Note: the what argument is not case-sensitive
(everything is converted to lower case.)
If TRUE, variable names are added to the vectors
and/or matrices.
If TRUE, vectors are given the `lavaan.vector' class;
matrices are given the `lavaan.matrix' class, and symmetric matrices are
given the `lavaan.matrix.symmetric' class. This only affects the way they
are printed on the screen.
Logical. Only used when the output are model matrices.
If TRUE, the model matrices are nested within groups. If FALSE,
a flattened list is returned containing all model matrices, with repeated
names for multiple groups.
If FALSE, the results are returned as
a list, where each element corresponds to a group (even if there is only
a single group.) If TRUE, the list will be unlisted if there is
only a single group.
The lavListInspect() and lavListTech() functions only differ in
the way they return the results. The lavListInspect() function will
prettify the output by default, while the lavListTech() will not attempt
to prettify the output by default.
Below is a list of possible values for the what argument, organized
in several sections:
Model matrices:
"free":A list of model matrices. The non-zero integers
represent the free parameters. The numbers themselves correspond
to the position of the free parameter in the parameter vector.
This determines the order of the model parameters in the output
of for example coef() and vcov().
"partable":A list of model matrices. The non-zero integers
represent both the fixed parameters (for example, factor loadings
fixed at 1.0), and the free parameters if we ignore any equality
constraints. They correspond with all entries (fixed or free)
in the parameter table. See parTable.
"start":A list of model matrices. The values represent
the starting values for all model parameters.
Alias: "starting.values".
Information about the data (including missing patterns):
"group":A character string. The group variable in the data.frame (if any).
"ngroups":Integer. The number of groups.
"group.label":A character vector. The group labels.
"cluster":A character vector. The cluster variable(s) in the data.frame (if any).
"ordered":A character vector. The ordered variables.
"nobs":Integer vector. The number of observations in each group that were used in the analysis (in each dataset).
"norig":Integer vector. The original number of observations in each group (in each dataset).
"ntotal":Integer. The total number of observations that
were used in the analysis. If there is just a single group, this
is the same as the "nobs" option; if there are multiple groups,
this is the sum of the "nobs" numbers for each group
(in each dataset).
Model features:
"meanstructure":Logical. TRUE if a meanstructure
was included in the model.
"categorical":Logical. TRUE if categorical endogenous
variables were part of the model.
"fixed.x":Logical. TRUE if the exogenous x-covariates
are treated as fixed.
"parameterization":Character. Either "delta" or
"theta".
"list":The parameter table. The same output as given
by parTable().
"options":List. The option list.
"call":List. The call as returned by match.call, coerced to a list.
# NOT RUN {
# fit model
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
# a data generating function
generateData <- function() simulateData(HS.model, sample.nobs = 100)
set.seed(1234)
fit <- semList(HS.model, dataFunction = generateData, ndat = 5,
store.slots = "partable")
# extract information
lavListInspect(fit, "free")
lavListTech(fit, "free")
# }
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